Alternative Instagram Memes: Intersectional Community and Collaborative Storytelling in the Digital Age
Bibliographic record
Abstract
Memes are an increasingly popular medium for self-expression in a digital context. On the social media site Instagram, queer and politically-left meme creators are subverting hegemonic power dynamics to present humorous and original memes, with sincere self-representation at their core: what I designate, alternative Instagram memes. Queer people often face discrimination and social exclusion in their local communities, exacerbated by the isolating effects of the COVID-19 pandemic, which leads many to seek out and forge digital communities of their own. In this research-creation project, I analyze the themes and discourse present in alternative Instagram memes posted by myself and by my peers to examine how this content and the community around it forms a digital intimate public. The expansion of #deardiarymemes, an interactive meme project based on anonymous confessions, exemplifies how memes function as digital storytelling tools and is central to this research. Through a curated series of memes by myself and by my peers as well as 41 new #deardiarymemes, this work builds on existing meme scholarship using feminist theorypractice to present a previously unstudied aspect of meme culture: a subversive, leftist meme community sprouting from the social media site Instagram.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".